%%
%% Copyright 2007, 2008, 2009 Elsevier Ltd
%%
%% This file is part of the 'Elsarticle Bundle'.
%% ---------------------------------------------
%%
%% It may be distributed under the conditions of the LaTeX Project Public
%% License, either version 1.2 of this license or (at your option) any
%% later version.  The latest version of this license is in
%%    http://www.latex-project.org/lppl.txt
%% and version 1.2 or later is part of all distributions of LaTeX
%% version 1999/12/01 or later.
%%
%% The list of all files belonging to the 'Elsarticle Bundle' is
%% given in the file `manifest.txt'.
%%

%% Template article for Elsevier's document class `elsarticle'
%% with harvard style bibliographic references
%% SP 2008/03/01
%%
%%
%%
%% $Id: elsarticle-template-harv.tex 4 2009-10-24 08:22:58Z rishi $
%%
%%
%% \documentclass[preprint,authoryear,12pt]{elsarticle}

%% Use the option review to obtain double line spacing
%% \documentclass[authoryear,preprint,review,12pt]{elsarticle}

%% Use the options 1p,twocolumn; 3p; 3p,twocolumn; 5p; or 5p,twocolumn
%% for a journal layout:
%% \documentclass[final,authoryear,1p,times]{elsarticle}
%% \documentclass[final,authoryear,1p,times,twocolumn]{elsarticle}
%% \documentclass[final,authoryear,3p,times]{elsarticle}
\documentclass[final,authoryear,3p,times,twocolumn]{elsarticle}
%% \documentclass[final,authoryear,5p,times]{elsarticle}
%% \documentclass[final,authoryear,5p,times,twocolumn]{elsarticle}

%% if you use PostScript figures in your article
%% use the graphics package for simple commands
%% \usepackage{graphics}
%% or use the graphicx package for more complicated commands
\usepackage{graphicx}
\usepackage{float}
%% or use the epsfig package if you prefer to use the old commands
%% \usepackage{epsfig}

%% The amssymb package provides various useful mathematical symbols
\usepackage{amsmath}
%% The amsthm package provides extended theorem environments
%% \usepackage{amsthm}

%% The lineno packages adds line numbers. Start line numbering with
%% \begin{linenumbers}, end it with \end{linenumbers}. Or switch it on
%% for the whole article with \linenumbers after \end{frontmatter}.
%% \usepackage{lineno}

%% natbib.sty is loaded by default. However, natbib options can be
%% provided with \biboptions{...} command. Following options are
%% valid:

%%   round  -  round parentheses are used (default)
%%   square -  square brackets are used   [option]
%%   curly  -  curly braces are used      {option}
%%   angle  -  angle brackets are used    <option>
%%   semicolon  -  multiple citations separated by semi-colon (default)
%%   colon  - same as semicolon, an earlier confusion
%%   comma  -  separated by comma
%%   authoryear - selects author-year citations (default)
%%   numbers-  selects numerical citations
%%   super  -  numerical citations as superscripts
%%   sort   -  sorts multiple citations according to order in ref. list
%%   sort&compress   -  like sort, but also compresses numerical citations
%%   compress - compresses without sorting
%%   longnamesfirst  -  makes first citation full author list
%%
%% \biboptions{longnamesfirst,comma}

\biboptions{sort&compress}

\usepackage{balance,psfrag,url}

\newcommand{\up}[1]{\raisebox{1.5ex}[0pt]{#1}}
\newcommand{\hyph}{\nobreakdash-\hspace{0pt}\relax}
\def\UrlFont{\normalsize\normalfont}

\journal{International Journal of Multimedia Data Engineering and Management}

\begin{document}

\begin{frontmatter}

%% Title, authors and addresses

%% use the tnoteref command within \title for footnotes;
%% use the tnotetext command for the associated footnote;
%% use the fnref command within \author or \address for footnotes;
%% use the fntext command for the associated footnote;
%% use the corref command within \author for corresponding author footnotes;
%% use the cortext command for the associated footnote;
%% use the ead command for the email address,
%% and the form \ead[url] for the home page:
%%
%% \title{Title\tnoteref{label1}}
%% \tnotetext[label1]{}
%% \author{Name\corref{cor1}\fnref{label2}}
%% \ead{email address}
%% \ead[url]{home page}
%% \fntext[label2]{}
%% \cortext[cor1]{}
%% \address{Address\fnref{label3}}
%% \fntext[label3]{}

\title{Navigating through Video Stories using Clustering Sets}

%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{<author name>}
%% \address[label1]{<address>}
%% \address[label2]{<address>}

\author{Sheila M. Pinto-C{\'{a}}ceres}
\ead{sheila.caceres@students.ic.unicamp.br}

\author{Jurandy Almeida\corref{cor}}
\ead{jurandy.almeida@ic.unicamp.br}

\author{V{\^{a}}nia P. A. Neris}
\ead{neris@ic.unicamp.br}

\author{Maria C. C. Baranauskas}
\ead{cecilia@ic.unicamp.br}

\author{Neucimar J. Leite}
\ead{neucimar@ic.unicamp.br}

\author{Ricardo da S. Torres}
\ead{rtorres@ic.unicamp.br}

\cortext[cor]{Corresponding author. Tel.: +55 19 3521-5887; 
Fax: +55 19 3521-5847}

\address{
Institute of Computing, University of Campinas -- UNICAMP \\
13083-852, Campinas, SP -- Brazil \\
}

\begin{abstract}
%% Text of abstract
Recent advances in technology have increased the availability of video 
data, creating a strong requirement for efficient systems to manage 
those materials. Making efficient use of video information requires 
that data be accessed in a user-friendly way. Ideally, one would like 
to perform video search using an intuitive tool. Most of existing 
browsers for the interactive search of video sequences, however, have 
employed a too rigid layout to arrange the results, restricting users 
to explore the results using list- or grid-based layouts. In this paper, 
we present a novel approach for the interactive search that displays 
the result set in a flexible manner. The proposed method is based on 
a simple and fast algorithm to build video stories and on an effective 
visual structure to arrange the storyboards, called Clustering Set. It 
is able to group together videos with similar content and to organize the 
result set in a well-defined tree. Results from a rigorous empirical 
comparison with a subjective evaluation show that such a strategy makes 
the navigation more coherent and engaging to users.

\end{abstract}

\begin{keyword}
%% keywords here, in the form: keyword \sep keyword
video browsing \sep interactive search \sep compressed domain \sep storyboards

%% MSC codes here, in the form: \MSC code \sep code
%% or \MSC[2008] code \sep code (2000 is the default)

\end{keyword}

\end{frontmatter}

%% \linenumbers

%% main text
%-------------------------------------------------------------------------
\section{Introduction}
% no \IEEEPARstart
Advances in data compression, data storage, and data transmission 
have facilitated the way videos are created, stored, and distributed. 
The increase in the amount of video data has enabled the creation 
of large digital video libraries. This has spurred great interest 
for systems that are able to efficiently manage video material~
\citep{TCSV_1998_Chang, SPIESR_1997_Hampapur, TMM_2007_Snoek}.

Making efficient use of video information requires that data to 
be accessed in a user-friendly way. For this, it is important to 
provide users with a browsing tool to interactively search for 
(or query) a video in large collections, without having to look 
through many possible results at the same time, so that a user 
can easily find the video in which he/she is interested. 

A lot of research has being done in browsing techniques for the 
interactive search of video sequences~\citep{CIVR_2008_Rooij, 
CIVR_2008_Zavesky, MIR_2008_Zavesky, TMM_2010_Rooij}. However, 
many of those research works have considered a rigid layout to 
arrange the result set in some default order, typically according 
to the relevance to the query.
 
In this paper, we present a novel approach for the interactive 
search that displays the result set in a more flexible and intuitive 
way. It relies on two key strategies: (1) storyboard generation and 
(2) visualization of stories. The former is a simple and fast algorithm 
to convert videos into storyboards. The speed up of the computation 
makes our technique suitable for browsing video content in online tasks. 
The latter is an effective visual structure to organize the video stories 
in a well-defined tree, called Clustering Set. This innovative framework 
is significant different from traditional paradigms, which often limit 
users to explore the results using list- or grid-based layouts.

Experiments were conducted both for evaluating the layout employed 
by the proposed method and for comparing it with several visualization 
techniques. Results from a subjective evaluation with 38 subjects 
show a clear preference by the display strategy of our approach.

The remainder of this paper is organized as follows. 
Section~\ref{sec:background} introduces the background of interactive 
search problems. Section~\ref{sec:ourapproach} presents our approach 
and shows how to apply it for browsing a large video collection. 
Section~\ref{sec:experiments} reports the results of our experiments 
and compares our technique with other methods. Finally, we offer our 
conclusions and directions for future work in Section~\ref{sec:conclusions}.

%-------------------------------------------------------------------------
\section{Background}
\label{sec:background}
The exploration of large collections of video data is non-trivial. 
When a user requests a search, the query formulation (search criterion) 
can be quite difficult.

Most of search systems are based on textual metadata, which leads 
to several problems when searching for visual content. Generally, 
the user lacks information about which keywords best represent the 
content that he/she is interested. In fact, different users tend 
to use different words to describe a same visual content. The lack of 
systematization in choosing query words can significantly affect 
the search results~\citep{CIVR_2008_Rooij}.

Modern systems have addressed those shortcomings by automatically 
detecting visual concepts derived from visual properties, such as 
color, texture, and shape. However, a minimum knowledge about the 
concept vocabulary is needed for performing a query, which is not 
appropriate for non-expert users~\citep{MIR_2008_Zavesky}.

Fully automated approaches have combined descriptors of multiple 
modalities (textual metadata, visual properties, and visual concepts). 
In spite of all the advances, the formulation of a query using such 
features is a difficult task for a human interested in a specific 
video~\citep{TMM_2010_Rooij}.

Once the search results are returned, we can explore many different 
directions based on query type and user intention. Several visualization 
techniques have been proposed to assist users in the exploration of 
result sets~\citep{CIVR_2008_Rooij, CIVR_2008_Zavesky, MIR_2008_Zavesky, 
TMM_2010_Rooij}. 

% A retrieval system needs to map a query result coherently in a visual 
% display over an available space to allow the user visualization using 
% several graphic features as position, size, color, shape and other 
% visual marks to represent each result element of the result set and an 
% overall structure to organize the set. Visualization process needs special 
% care because the use of an adequate visualization technique will provide 
% more information spending lower time.

Those methods often employ dimensionality reduction algorithms 
to map the high-dimensional feature space of visual properties 
into a fixed display. Afterwards, a display strategy is applied 
for producing user-browsable content~\citep{CIVR_2008_Zavesky}. 

There are two basic kinds of navigation~\citep{CIVR_2008_Rooij}: 
targeted search and exploratory search. The former performs a fast 
browsing in a single list of results. The latter allows the user 
to control the browsing procedure in several ways.

The major challenge of designing an interactive display is the 
fatigue and frustration that a user might experience. In general, 
users can spend a limited time to identify relevant videos for a 
query, thus they are hard-pressed to quickly inspect a large set 
of results.

The layout of videos is another concern for an interactive system. 
An effective tool for browsing in large collections should be suited 
for users without any expertise, providing an easy way to use the 
interface.

Most of existing approaches use list- or grid-based 
layouts, where the videos are disposed in a linear or 
grid manner according to their relevance to the query 
pattern~\citep{COMPUTER_1995_Flickner, SPIESR_1997_Hampapur}. 
Several techniques display the result set in a circular 
or elliptical form. Those radial methods try to centralize 
the user vision by setting the query at the center of the 
available space and then place similar videos around it, 
allowing easy access and exploration of the result 
set~\citep{interface-firststep-human-centered, 
interface-cluster-human-centric02, interface-cluster-human-centric04}.
Other methods exploit clustering algorithms in order to analyze 
the similarities between all the search results and, hence, display 
similar videos of the result set close to each 
other~\citep{interface-cluster-human-centric04, 
interface-cluster-nguyen, interface-cluster-visualization}. 

On one hand, grid- and radial-based structures offer a good navigability 
and exploration of the result set in an organized way. However, they 
generally do not respect the intrinsic relationship among the results, 
displaying different similarity degrees at the same physical distance 
to the query pattern. On the other hand, cluster-based techniques
provide a better understanding of the universe of available results. 
This approach usually focuses on a great amount of information without, 
in many cases, take care of an adequate design to distribute the 
clusters, which entails confusion for the users.

Different from all previous works, our approach combines the advantages 
of those approaches into a single structure, called Clustering Set. This 
strategy allows users to explore the result set in a more flexible and 
intuitive way. 

%-------------------------------------------------------------------------
\section{Our Approach}
\label{sec:ourapproach}
Save time in browsing, intuitively comprehend the results, and allow 
an exploratory search: those are the basic principles of our approach.
In the following subsections, different design choices to achieve such 
goals are discussed in more details.

\subsection{Features and Similarity}
Humans judge more quickly the relevance of interrelated items. However, 
discovering the ideal relationship for such a judgement is non-trivial. 
The simplest approach is to group together video frames with similar 
content, so that a relevant judgement for one video frame could be 
applied to all near-duplicated ones and, hence, maximize the diversity.

In our approach, stories are the meaningful and manageable units 
for presenting the result set to the user. They consist of multiple 
shots and are represented by a collection of frames, as illustrated 
in Figure~\ref{fig:storyboard}. This strategy provides an easy way 
for the user to visually judge whether a story is worth exploring.

\begin{figure}[htb]
  \centering
  \begin{tabular}{c}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/01.eps}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/02.eps}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/03.eps}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/04.eps} \\
    \includegraphics[width=0.1\textwidth]{pics/storyboard/05.eps}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/06.eps}
    \includegraphics[width=0.1\textwidth]{pics/storyboard/07.eps}
  \end{tabular}
  \caption{An example of storyboard produced for the video \textit{Senses 
           And Sensitivity, Introduction to Lecture 3 presenter}.}
  \label{fig:storyboard}
\end{figure}

We adopt a simple and fast algorithm to build storyboards described 
in~\citep{ISM_2010_Almeida}. This technique was designed to be simple 
and efficient in order to produce video stories in a reasonable time 
and with an acceptable quality, so as to allow online usage. It consists 
of three main steps: (1) feature extraction; (2) content selection; 
and (3) noise filtering. A flowchart of this approach is shown in 
Figure~\ref{fig:flowchart}. 

For each frame of an input sequence, visual features are extracted 
from the video stream for describing its visual content. After that, 
a simple and fast algorithm is used to detect groups of video frames 
with a similar content and for selecting a representative frame per 
each group. Finally, the selected frames are filtered in order to 
avoid possible redundant or meaningless frames in the storyboard. 
For a detailed discussion of this procedure, refer to~\citep{ISM_2010_Almeida}.

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{Label1}[cc][cc][0.8]{Input Sequence}
  \psfrag{Label2}[cc][cc][0.8]{Feature Extraction}
  \psfrag{Label3}[cc][cc][0.8]{Content Selection}
  \psfrag{Label4}[cc][cc][0.8]{Noise Filtering}
  \psfrag{Label5}[cc][cc][0.8]{Last Frame?}
  \psfrag{Label6}[cc][cc][0.8]{Video Summary}
  \psfrag{Label7}[cc][cc][0.8]{No}
  \psfrag{Label8}[cc][cc][0.8]{Yes}
  \includegraphics[width=0.7\columnwidth]{pics/flowchart/flowchart.eps}
  \psfragscanoff
  \caption{Flowchart of the method used to build 
           storyboards~\citep{ISM_2010_Almeida}.}
  \label{fig:flowchart}
\end{figure}

Numerous forms of raw features can be used to determine the similarity 
between video frames of different storyboards. Each type of feature spans 
a multidimensional feature space. The distance function determines the 
dissimilarity between features within this space. Thus, we coordinate the 
display positions of each story using its dissimilarity space. 

Our technique was designed to be flexible and robust and, therefore, 
the feature input is not limited to a specific type. Instead, all 
possible data types can be used. The only requirement is that the 
dissimilarity between features must be numerically represented by 
an appropriate distance metric. 

\subsection{Intuitive Display}
A problem regarding the interactive search of video sequences is 
the human understanding of what the system was trying to judge as 
relevant. 

The most common approach for designing an interactive 
display is to use dimensionality reduction algorithms to map the 
multidimensional feature space into a fixed display and to apply 
a display strategy for producing user-browsable content. 

The key advantage of our technique is to merge the positive aspects 
of different visualization strategies into a single structure, as 
illustrated in Figure~\ref{fig:clusterset}. Such a visualization design 
is an original contribution of this work, called Clustering Set. It groups 
similar results as a cluster set, which is displayed in a radial manner 
and without overlapping. In this way, we take the advantages of a radial 
distribution while preserving relationships between similar videos. This 
strategy allows the user to view the relationship between several clusters 
at once, providing a comfortable exploration and a better navigability.

\begin{figure*}[!htb]
  \centering
  \includegraphics[width=0.9\textwidth]{pics/captures/clusterset.eps}
  \caption{An example of the visualization of our approach.}
  \label{fig:clusterset}
\end{figure*}

In this figure, we display the stories for a sample of 50 
videos randomly selected from the Open Video Project\footnote{
\url{http://www.open-video.org/}}. All videos are in MPEG-1 
format (at 352 $\times$ 240 resolution and 29.97 frames per 
second), in color and with sound. The selected videos 
are distributed among several genres (e.g., documentary, 
educational, ephemeral, historical, lecture) and their 
duration varies from 1 to 4 minutes. Those videos are the 
same used in~\citep{ISM_2010_Almeida} and their storyboards 
can be seen at \url{http://www.liv.ic.unicamp.br/~jurandy/summaries/}.

We converted each frame of those storyboards to a 64\hyph dimensional 
feature vector by computing a Color Histogram~\citep{IJCV_1991_Swain}. 
The color histograms were extracted as follows: the RGB space is 
divided into 64 subspaces (colors), using four ranges in each 
color channel. The value for each dimension of a feature vector 
is the density of each color in the entire frame. The distance 
function used to compare the feature vectors is the Manhattan 
($L_1$) distance.

Our approach places the query in the center of the visualization display. 
Thus, we force the user to focus his/her attention on the center of the 
screen. The clusters of a given level are circularly distributed around 
the query in a clockwise order of similarity regarding the query, which 
is represented by the width of the connecting line between them. 

\begin{figure*}[!htb]
  \centering
  \psfragscanon
  \psfrag{Label1}[cc][cc][1.0]{\bf\sc Timeline}
  \psfrag{Label3}[cc][cc][1.0][45]{\bf\sc Query}
  \includegraphics[width=0.9\textwidth]{pics/captures/browsing.eps}
  \psfragscanoff
  \caption{Overview of the navigation options of our approach.}
  \label{fig:browsing}
\end{figure*}

This strategy is also applied to each of the clusters. In this way, 
the user has a more intuitive understanding of the display. At the center, 
we present the most relevant result. It represents the centroid of its 
cluster. The remaining results are sorted in a circular manner according 
to a clockwise order of similarity with respect to the centroid, which 
is denoted by their size and border color (in a color gamma from yellow 
to dark green).

In this way, we provide a coherent distribution of the query-related 
video universe by setting the results over a well-organized structure. 
This distribution, in most cases, avoid overlapping, which represents 
a valuable advantage over other cluster-based visualization 
techniques~\citep{interface-cluster-nguyen,interface-cluster-visualization,interface-cluster-human-centric04}.

\subsection{Engaged and Guided Browsing}
The fully utilization of a user's inspection ability requires an 
engaging display which is guided by user preferences. Our approach 
fulfill such a principle by dynamically rearranging the result set.
Figure~\ref{fig:browsing} presents the navigation options of the 
propose method. Those options indicate all the possible browsing 
directions of a user.

Using a mouse click or a key press, the user can give an indication 
of which story is the most relevant. Then, we place the user at a 
new set of results which is most related to the last story marked 
as relevant, as illustrated in the right column of the 
Figure~\ref{fig:browsing}.
 
Our approach is totally flexible, allowing users to navigate laterally 
in the timeline of the video. Thus, the result set is updated whenever 
they decide to focus in another story of a video. The top line of the 
Figure~\ref{fig:browsing} illustrates such a transition. In this way, 
the user can combine both targeted search and temporal browsing, which 
often yields more relevant results.

\begin{figure*}[!htb]
  \centering
  \includegraphics[width=0.9\textwidth]{pics/captures/window.eps}
  \caption{The pop-up window where a specific story is handled.}
  \label{fig:window}
\end{figure*}

Additionally, we integrate different functionalities in the interface. By 
right-clicking on the stories, the user is presented with the operations 
that can be performed to them. This opens a pop-up window on the screen, 
as illustrated in Figure~\ref{fig:window}. On the top, we make available 
a video player. Below, we display a click-able sequence of story collages 
and the selected story is highlighted in the video timeline. The frames 
from the shots in the selected story are expanded on the bottom.

Anytime users can also change the video-of-interest by choosing any of 
the stories visible in the screen. Thus, they can interleave between 
targeted search and exploratory search. Using different searching 
and browsing methods into a single environment enhance the user's inspection 
ability. In this way, the user is in complete control and can change 
the current view at any time.

%-------------------------------------------------------------------------
\section{Experimental Evaluation}
\label{sec:experiments}
In this section, we evaluate and compare the layout employed 
by the proposed method with previous work in visualization 
strategies. 

\subsection{Visual Structures}
\label{sec:visualstructures}
This section presents some visualization techniques widely used 
in the literature. In Section~\ref{sec:results}, we compare these 
approaches to the proposed method. 

\subsubsection{Grid}
The most common approach employed to organize a set of results are 
the grid-based layouts~\citep{COMPUTER_1995_Flickner, SPIESR_1997_Hampapur}. 
This method disposes the result set in a matricial form. The query is 
placed on top left and successive positions are sorted from left-to-right 
and top-to-down according to their relevance, as illustrated in 
Figure~\ref{fig:visualstructure:grid}.

\begin{figure}[htb]
  \centering
  \includegraphics[width=0.3\textwidth]{pics/visualstructures/grid.eps}
  \caption{An example of a grid-based layout.}
  \label{fig:visualstructure:grid}
\end{figure}

\subsubsection{Concentric Rings}
\citet{CIKM_2003_Torres} introduced a visual structure that arranges the 
result set in a series of concentric rings. In this way, the most relevant 
results for the query (centroid) are located over a nearer ring, as 
showed in Figure~\ref{fig:visualstructure:rings}.

\begin{figure}[htb]
  \centering
  \includegraphics[width=0.3\textwidth]{pics/visualstructures/rings.eps}
  \caption{An example of a visual structure based on concentric rings.}
  \label{fig:visualstructure:rings}
\end{figure}

\subsubsection{Spiral}
\citet{CIKM_2003_Torres} proposed to organize the result set over a 
spiral structure. Thus, the query is placed at the origin of the spiral 
and successive results are distributed over the spiral line in increase 
order of relevance, as as presented in Figure~\ref{fig:visualstructure:spiral}. 

\begin{figure}[htb]
  \centering
  \includegraphics[width=0.3\textwidth]{pics/visualstructures/spiral.eps}
  \caption{An example of a spiral-based layout.}
  \label{fig:visualstructure:spiral}
\end{figure}

\subsection{Experimental Protocol}
Unlike other research areas, evaluating a display strategy is not a 
straightforward task due to the lack of an objective ground-truth. 
A consistent evaluation framework is seriously missing for visualization 
research. Presently, every work has its own evaluation methodology, often 
presented without any performance analysis. 

In this work, we adopted a evaluation framework known as 
\emph{DECIDE}~\citep{interaction-design}. It guides the 
experimental evaluation through six well-defined steps:      
\textbf{D}etermine the goals, 
\textbf{E}xplore the questions, 
\textbf{C}hoose the evaluation paradigm and techniques, 
\textbf{I}dentify the practical issues, 
\textbf{D}ecide how to deal with the ethical issues, 
\textbf{E}valuate, interpret, and present the data.      

In the following subsections, each of those steps is explained in more detail.

\subsubsection{\textbf{D}etermine the goals}      
The goal of the experiment is to validate the visual structure 
named Clustering Set. We are convinced that such a visualization 
technique will be a valuable contribution for future video browsing 
and retrieval systems.

\subsubsection{\textbf{E}xplore the questions} 
\label{sub:questions}
In order to achieve our goals, we defined some questions for assessing 
the user preferences regarding the introduced technique:

\begin{itemize}
  \item Is the user satisfied with the presented layout?
  \item Is it possible to understand the visual structure and 
        how it distributes the result set?
  \item Is it possible to identify the query pattern?
  \item Is it possible to recognize where most relevant results are placed?
  \item Is it possible to recognize where least relevant results are placed?
\end{itemize}

For obtaining unaware information, we also consider open questions 
for general comments about the evaluated approaches.

\subsubsection{\textbf{C}hoose the evaluation paradigm and techniques}
In this step, we adopted the Usability Test method (the standard 
ISO 9241). The evaluation instrument used in our experiments was 
a questionnaire. In this way, we are able to analyze a bigger sample 
in a faster way.

\subsubsection{\textbf{I}dentify the practical issues}
\label{sub:practical}
\begin{itemize}

  \item \textbf{Users.} The experimental evaluation was made with the 
        collaboration of 38 volunteers from three different courses from 
        the Institute of Computing at the University of Campinas. In order to 
        obtain significant results, a set of collaborators with a previous 
        knowledge in the visualization area were invited to take part in our 
        experiments. 

  \item \textbf{Equipment.} Each experiment was carried out in several 
        laboratories of the Institute of Computing at University of Campinas. 
        Each participant had access to one computer and was free to use 
        the operating system he/she prefers.

  \item \textbf{Material.} Each participant received a set of documents 
        before evaluation starts. This set included:
        \begin{itemize}
          \item \textit{User Instructions.} It details what the user 
                needs to know as experiment steps, available time, ethical 
                issues, etc.
          \item \textit{Free and Clear Consent Terms.} It is a mandatory 
                certificate in every investigation involving users. This 
                document was signed by each user as a term of conformity 
                with the experiments conditions.
          \item \textit{User Profile Form.} It allows to register relevant 
                data of an user, such as his/her familiarity with the system 
                and frequency of using the computer.
          \item \textit{Evaluation Form.} This is the main document of the 
                experiment where the user opinion is captured. Basically, by 
                using this form, users answer the questions presented in 
                Section~\ref{sub:questions}.
        \end{itemize}
\end{itemize}

\subsubsection{\textbf{D}ecide how to deal with the ethical issues}
Ethical questions were clearly explained to the user verbally and in 
the user instructions. It was also specified in the Free and Clear 
Consent Terms, which was signed by the user as an acceptance condition.

\subsubsection{\textbf{E}valuate, interpret, and present the data}
The experiments were conducted at a laboratory of the university on a
predefined hour. All users were invited to participate in the experiment.
Users who accepted the invitation were free to choose a computer and 
an operational system. Initially, we gave to the users a set of documents 
described in Section~\ref{sub:practical}. Then, users read and signed 
the Free and Clear Consent Terms. After that, they filled the user 
profile form. Finally, they downloaded the structure prototipes
and evaluate the visual structures by answering an evaluation form.

We analyzed five criteria: 
%
\begin{enumerate}
  \item \emph{SA} -- satisfaction with the layout,
  \item \emph{UD} -- understanding degree, 
  \item \emph{QP} -- easiness of finding the query pattern, 
  \item \emph{MR} -- easiness of finding the most relevant results, 
  \item \emph{LR} -- easiness of finding the least relevant results. 
\end{enumerate}

Each one of the 38 participants evaluated each of those criteria by 
allocating a number from 1 to 5 according to their opinion, where 1 
represents the worst qualification and 5 represents the best one. 

\subsection{Experimental Results}
\label{sec:results}
Figures~\ref{fig:satisfaction}-\ref{fig:lessrelevants} compare different 
visual structures. Those graphs present an overall analysis of each criterion 
using boxplots. Figure~\ref{fig:tukey} gives an explanation of the conventions 
used in the Tukey-style boxplots. The results indicate that the Clustering Set 
performs better than all the compared methods. Notice that it obtained the 
best punctuation on all the criteria. In addition, our approach present 
the lowest dispersion of data. It means that, in general, the users are 
agree on the score of the Clustering Set. 

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{Maximum}[cl][cl][0.6]{\textbf{Maximum}}
  \psfrag{Median}[cr][cr][0.6]{\textbf{Median}}
  \psfrag{Confidence}[cl][cl][0.6]{\textbf{95\% Confidence Interval}}
  \psfrag{Minimum}[cl][cl][0.6]{\textbf{Minimum}}
  \psfrag{Outlier}[cl][cl][0.6]{\textbf{Outlier}}
  \psfrag{LabelX}[tc][tc][0.8]{\textit{Method}}
  \psfrag{LabelY}[bc][bc][0.8]{\textit{Measure}}
  \psfrag{0.0}[cc][cc][0.6]{0.0}
  \psfrag{0.2}[cc][cc][0.6]{0.2}
  \psfrag{0.4}[cc][cc][0.6]{0.4}
  \psfrag{0.6}[cc][cc][0.6]{0.6}
  \psfrag{0.8}[cc][cc][0.6]{0.8}
  \psfrag{1.0}[cc][cc][0.6]{1.0}
  \includegraphics[width=0.8\columnwidth]{pics/tukey/tukey.eps}
  \psfragscanoff
  \caption{An example of a Tukey-style boxplot.}
  \label{fig:tukey}
\end{figure}

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{LabelX}[tc][tc][0.8]{(1) Unsatisfied -- (5) Satisfied}
  \psfrag{Method1}[bc][bc][0.6]{Grid}
  \psfrag{Method2}[bc][bc][0.6]{Concentric Rings}
  \psfrag{Method3}[bc][bc][0.6]{Spiral}
  \psfrag{Method4}[bc][bc][0.6]{Clustering Set}
  \psfrag{1}[tc][tc][0.6]{1}
  \psfrag{2}[tc][tc][0.6]{2}
  \psfrag{3}[tc][tc][0.6]{3}
  \psfrag{4}[tc][tc][0.6]{4}
  \psfrag{5}[tc][tc][0.6]{5}
  \includegraphics[width=0.8\columnwidth]{pics/plots/satisfaction.eps}
  \psfragscanoff
  \caption{Satisfaction with the disposition of the results (\emph{SA}).}
  \label{fig:satisfaction}
\end{figure}

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{LabelX}[tc][tc][0.8]{(1) Incomprehensible -- (5) Understandable}
  \psfrag{Method1}[bc][bc][0.6]{Grid}
  \psfrag{Method2}[bc][bc][0.6]{Concentric Rings}
  \psfrag{Method3}[bc][bc][0.6]{Spiral}
  \psfrag{Method4}[bc][bc][0.6]{Clustering Set}
  \psfrag{1}[tc][tc][0.6]{1}
  \psfrag{2}[tc][tc][0.6]{2}
  \psfrag{3}[tc][tc][0.6]{3}
  \psfrag{4}[tc][tc][0.6]{4}
  \psfrag{5}[tc][tc][0.6]{5}
  \includegraphics[width=0.8\columnwidth]{pics/plots/understanding.eps}
  \psfragscanoff
  \caption{Understanding degree (\emph{UD}).}
  \label{fig:understanding}
\end{figure}

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{LabelX}[tc][tc][0.8]{(1) Difficult -- (5) Easy}
  \psfrag{Method1}[bc][bc][0.6]{Grid}
  \psfrag{Method2}[bc][bc][0.6]{Concentric Rings}
  \psfrag{Method3}[bc][bc][0.6]{Spiral}
  \psfrag{Method4}[bc][bc][0.6]{Clustering Set}
  \psfrag{1}[tc][tc][0.6]{1}
  \psfrag{2}[tc][tc][0.6]{2}
  \psfrag{3}[tc][tc][0.6]{3}
  \psfrag{4}[tc][tc][0.6]{4}
  \psfrag{5}[tc][tc][0.6]{5}
  \includegraphics[width=0.8\columnwidth]{pics/plots/queryimage.eps}
  \psfragscanoff
  \caption{Easiness of finding the query pattern (\emph{QP}).}
  \label{fig:queryimage}
\end{figure}

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{LabelX}[tc][tc][0.8]{(1) Difficult -- (5) Easy}
  \psfrag{Method1}[bc][bc][0.6]{Grid}
  \psfrag{Method2}[bc][bc][0.6]{Concentric Rings}
  \psfrag{Method3}[bc][bc][0.6]{Spiral}
  \psfrag{Method4}[bc][bc][0.6]{Clustering Set}
  \psfrag{1}[tc][tc][0.6]{1}
  \psfrag{2}[tc][tc][0.6]{2}
  \psfrag{3}[tc][tc][0.6]{3}
  \psfrag{4}[tc][tc][0.6]{4}
  \psfrag{5}[tc][tc][0.6]{5}
  \includegraphics[width=0.8\columnwidth]{pics/plots/morerelevants.eps}
  \psfragscanoff
  \caption{Easiness of finding the most relevant results (\emph{MR}).}
  \label{fig:morerelevants}
\end{figure}

\begin{figure}[htb]
  \centering
  \psfragscanon
  \psfrag{LabelX}[tc][tc][0.8]{(1) Difficult -- (5) Easy}
  \psfrag{Method1}[bc][bc][0.6]{Grid}
  \psfrag{Method2}[bc][bc][0.6]{Concentric Rings}
  \psfrag{Method3}[bc][bc][0.6]{Spiral}
  \psfrag{Method4}[bc][bc][0.6]{Clustering Set}
  \psfrag{1}[tc][tc][0.6]{1}
  \psfrag{2}[tc][tc][0.6]{2}
  \psfrag{3}[tc][tc][0.6]{3}
  \psfrag{4}[tc][tc][0.6]{4}
  \psfrag{5}[tc][tc][0.6]{5}
  \includegraphics[width=0.8\columnwidth]{pics/plots/lessrelevants.eps}
  \psfragscanoff
  \caption{Easiness of finding the least relevant results (\emph{LR}).}
  \label{fig:lessrelevants}
\end{figure}

In order to verify the statistical significance of those results, 
the confidence intervals for the differences between paired medians 
were computed to compare every pair of methods. If the confidence 
interval includes zero, the difference is not significant at that 
confidence level. If the confidence interval does not include zero, 
then the sign of the median difference indicates which alternative 
is better~\citep{BOOK_1991_Jain}. 

Table~\ref{tab:confidence} presents the confidence intervals (with 
a confidence of 95\%) for the differences between our technique and 
previous work. The analysis of the experiment shows that there is no
significant difference between the Clustering Set and the Grid method 
with respect to the \emph{UD}~(understanding degree) and 
\emph{QP}~(easiness of finding the query pattern) criteria.  
Since the confidence intervals do not include zero for the other 
criteria, the results confirm that the Clustering Set outperforms 
all other methods regarding the satisfaction of the users with the 
layout~(\emph{SA}) and the easiness of finding the most relevant 
results~(\emph{MR}).

\begin{table*}[!htb]
  \centering
  \caption{Differences between our technique and previous work at a 
           confidence of 95\%.}
  \begin{tabular}{c|cc|cc|cc}
    \hline
    \hline
    & \multicolumn{2}{c|}{\textbf{Clustering Set - Grid}} & 
      \multicolumn{2}{c|}{\textbf{Clustering Set - Concentric Rings}} &
      \multicolumn{2}{c}{\textbf{Clustering Set - Spiral}} \\
    \cline{2-7}
    \up{\textbf{Criterion}} & 
    \textit{min.} & \textit{max.} & 
    \textit{min.} & \textit{max.} & 
    \textit{min.} & \textit{max.} \\
    \hline
    \emph{SA} &  0.231 & 1.769 &  1.231 & 2.769 &  0.975 & 3.025 \\
    \emph{UD} & -0.513 & 0.513 &  0.231 & 1.769 & -0.025 & 2.025 \\
    \emph{QP} & -0.269 & 1.269 &  0.231 & 1.769 &  0.231 & 1.769 \\
    \emph{MR} &  1.487 & 2.513 &  0.487 & 1.513 &  1.744 & 2.256 \\
    \emph{LR} &  0.975 & 3.025 & -0.025 & 2.025 & -0.282 & 2.282 \\
    \hline
    \hline
  \end{tabular}
  \label{tab:confidence}
\end{table*}

Finally, our approach also shared the best qualification with several 
other methods in relation to the \emph{LR} (easiness of finding the least 
relevant results) criterion. Considering those observations, it is possible 
to conclude that the Clustering Set is a worth-full contribution for the 
result set presentation, visualization and exploration of a retrieval system.

Open questions help us to identify users opinions and aspects to take into 
account in future works. In general, the users' comments were favorable for 
the proposed method. Most of the comments were suggestions to extend or to 
add features to the Clustering Set, which confirms the interest of the users 
in our approach. One common suggestion was the possible combination with 
other techniques, creating an hybrid strategy.

Visual features are also relevant issues to take into account. For example, 
the background color used by the Concentric Rings helped to understand the 
distribution of the results. Users suggested to use of the same characteristic 
in the Spiral method. For that reason, the Concentric Rings achieved a sightly 
better scoring than the Spiral method. Eventually, this feature can be also 
included into the Clustering Set.

%-------------------------------------------------------------------------
\section{Conclusions}
\label{sec:conclusions}
In this paper, we have presented a novel approach for the interactive 
search that displays the result set in a more flexible and intuitive 
way. Our technique relies both on a simple and fast algorithm to build 
storyboards and on a hierarchical structure named Clustering Set, where 
visually or semantically alike video stories are placed together. Such 
a strategy offers a guided browsing more coherent and engaging to users. 
These benefits were carefully demonstrated in our showcases.

We have performed a rigorous experimental design both for evaluating the 
layout employed by the proposed method and for comparing it with several 
visualization techniques. Results from a subjective evaluation with 38 
subjects have shown that our approach clearly outperforms the most of 
the compared methods regarding the most of the evaluated criteria.

Future work includes a subjective evaluation of the whole approach. 
In addition, we plan to augment the proposed method for considering 
local features~\citep{SAC_2008_Almeida} and/or motion 
analysis~\citep{ISVC_2009_Almeida, IWSSIP_2010_Almeida}.
We also want to augment the Clustering Set by including different 
types of visualization, creating a hybrid structure, as suggested 
by the users. Moreover, we intend to evaluate other visual features 
and similarity metrics. Finally, we plan to investigate the effects 
of integrating our technique into a complete system for 
search-and-retrieval of video sequences. 

\section*{Acknowledgments}
We would like to thank to all the participants of the experiments for 
their valuable contributions. This research was supported by Brazilian 
agencies FAPESP (Grant~07/52015-0, 08/50837-6, 09/04732-0, and 09/18438-7), 
CNPq (Grant~311309/2006-2, 472402/2007-2, 135526/2008-6, and 306587/2009-2), 
and CAPES (Grant~01P-05866/2007).

%% The Appendices part is started with the command \appendix;
%% appendix sections are then done as normal sections
%% \appendix

%% \section{}
%% \label{}

%% References
%%
%% Following citation commands can be used in the body text:
%%
%%  \citet{key}  ==>>  Jones et al. (1990)
%%  \citep{key}  ==>>  (Jones et al., 1990)
%%
%% Multiple citations as normal:
%% \citep{key1,key2}         ==>> (Jones et al., 1990; Smith, 1989)
%%                            or  (Jones et al., 1990, 1991)
%%                            or  (Jones et al., 1990a,b)
%% \cite{key} is the equivalent of \citet{key} in author-year mode
%%
%% Full author lists may be forced with \citet* or \citep*, e.g.
%%   \citep*{key}            ==>> (Jones, Baker, and Williams, 1990)
%%
%% Optional notes as:
%%   \citep[chap. 2]{key}    ==>> (Jones et al., 1990, chap. 2)
%%   \citep[e.g.,][]{key}    ==>> (e.g., Jones et al., 1990)
%%   \citep[see][pg. 34]{key}==>> (see Jones et al., 1990, pg. 34)
%%  (Note: in standard LaTeX, only one note is allowed, after the ref.
%%   Here, one note is like the standard, two make pre- and post-notes.)
%%
%%   \citealt{key}          ==>> Jones et al. 1990
%%   \citealt*{key}         ==>> Jones, Baker, and Williams 1990
%%   \citealp{key}          ==>> Jones et al., 1990
%%   \citealp*{key}         ==>> Jones, Baker, and Williams, 1990
%%
%% Additional citation possibilities
%%   \citeauthor{key}       ==>> Jones et al.
%%   \citeauthor*{key}      ==>> Jones, Baker, and Williams
%%   \citeyear{key}         ==>> 1990
%%   \citeyearpar{key}      ==>> (1990)
%%   \citetext{priv. comm.} ==>> (priv. comm.)
%%   \citenum{key}          ==>> 11 [non-superscripted]
%% Note: full author lists depends on whether the bib style supports them;
%%       if not, the abbreviated list is printed even when full requested.
%%
%% For names like della Robbia at the start of a sentence, use
%%   \Citet{dRob98}         ==>> Della Robbia (1998)
%%   \Citep{dRob98}         ==>> (Della Robbia, 1998)
%%   \Citeauthor{dRob98}    ==>> Della Robbia


%% References with bibTeX database:
\balance
%\bibliographystyle{elsarticle-harv}
%\bibliography{articlelongstrings,article,book,incollection,proceedingslongstrings,inproceedings,proceedings,mastersthesis,phdthesis,techreport,sibgrapi2010-sheila}

%% Authors are advised to submit their bibtex database files. They are
%% requested to list a bibtex style file in the manuscript if they do
%% not want to use elsarticle-harv.bst.

%% References without bibTeX database:

% \begin{thebibliography}{00}

%% \bibitem must have one of the following forms:
%%   \bibitem[Jones et al.(1990)]{key}...
%%   \bibitem[Jones et al.(1990)Jones, Baker, and Williams]{key}...
%%   \bibitem[Jones et al., 1990]{key}...
%%   \bibitem[\protect\citeauthoryear{Jones, Baker, and Williams}{Jones
%%       et al.}{1990}]{key}...
%%   \bibitem[\protect\citeauthoryear{Jones et al.}{1990}]{key}...
%%   \bibitem[\protect\astroncite{Jones et al.}{1990}]{key}...
%%   \bibitem[\protect\citename{Jones et al., }1990]{key}...
%%   \harvarditem[Jones et al.]{Jones, Baker, and Williams}{1990}{key}...
%%

% \bibitem[ ()]{}

% \end{thebibliography}

\begin{thebibliography}{21}
\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi
\expandafter\ifx\csname url\endcsname\relax
  \def\url#1{\texttt{#1}}\fi
\expandafter\ifx\csname urlprefix\endcsname\relax\def\urlprefix{URL }\fi

\bibitem[{Almeida et~al.(2009)Almeida, Minetto, Almeida, Torres, and
  Leite}]{ISVC_2009_Almeida}
Almeida, J., Minetto, R., Almeida, T.~A., Torres, R.~S., Leite, N.~J., 2009. 
  Robust estimation of camera motion using optical flow models.
  In: Proceedings of the International Symposium on Advances in
  Visual Computing (ISVC'09), pp. 435--446.

\bibitem[{Almeida et~al.(2010{\natexlab{a}})Almeida, Minetto, Almeida, Torres,
  and Leite}]{IWSSIP_2010_Almeida}
Almeida, J., Minetto, R., Almeida, T.~A., Torres, R.~S., Leite, N.~J., 
  2010{\natexlab{a}}. Estimation of camera parameters in video sequences
  with a large amount of scene motion. In: Proceedings of the {IEEE}
  International Conference on Systems, Signals and Image Processing 
  (IWSSIP'10), pp. 348--351.

\bibitem[{Almeida et~al.(2008)Almeida, Rocha, Torres, and
  Goldenstein}]{SAC_2008_Almeida}
Almeida, J., Rocha, A., Torres, R.~S., Goldenstein, S., 2008.
  Making colors worth more than a thousand words. In: Proceedings of the 
  {ACM} International Symposium on  Applied Computing (ACM SAC'08), 
  pp. 1180--1186.

\bibitem[{Almeida et~al.(2010{\natexlab{b}})Almeida, Torres, and
  Leite}]{ISM_2010_Almeida}
Almeida, J., Torres, R.~S., Leite, N.~J., 2010{\natexlab{b}}. Rapid
  video summarization on compressed video. In: Proceedings of the {IEEE}
  International Symposium on Multimedia (ISM'10), pp. 113--120.

\bibitem[{Chang et~al.(1998)Chang, Chen, Meng, Sundaram, and
  Zhong}]{TCSV_1998_Chang}
Chang, S.-F., Chen, W., Meng, H.~J., Sundaram, H., Zhong, D., 1998. A
  fully automated content-based video search engine supporting spatio-temporal
  queries. {IEEE} Transactions on Circuits Systems and Video Technology 8~(5),
  602--615.

\bibitem[{Chen et~al.(2000)Chen, Gagaudakis, and
  Rosin}]{interface-cluster-visualization}
Chen, C., Gagaudakis, G., Rosin, P., 2000. Content-based image visualization.
  In: Proceedings of the International Conference on Information
  Visualisation (IV'00), pp. 13--18.

\bibitem[{{De Rooij} et~al.(2008){De Rooij}, Snoek, and
  Worring}]{CIVR_2008_Rooij}
{De Rooij}, O., Snoek, C. G.~M., Worring, M., 2008. Balancing thread
  based navigation for targeted video search. In: Proceedings of the {ACM}
  International Conference on Image and Video Retrieval (CIVR'08), 
  pp. 485--494.

\bibitem[{{De Rooij} and Worring(2010)}]{TMM_2010_Rooij}
{De Rooij}, O., Worring, M., 2010. Browsing video along multiple threads.
  {IEEE} Transactions on Multimedia 12~(2), 121--130.

\bibitem[{Flickner et~al.(1995)Flickner, Sawhney, Ashley, Huang, Dom, Gorkani,
  Hafner, Lee, Petkovic, Steele, and Yanker}]{COMPUTER_1995_Flickner}
Flickner, M., Sawhney, H.~S., Ashley, J., Huang, Q., Dom, B., Gorkani, M.,
  Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P., 1995. Query
  by image and video content: The {QBIC} system. {IEEE} Computer 28~(9),
  23--32.

\bibitem[{Guerin-Dugue et~al.(2003)Guerin-Dugue, Ayache, and
  Berrut}]{interface-firststep-human-centered}
Guerin-Dugue, A., Ayache, S., Berrut, C., 2003. Image retrieval: a first
  step for a human centered approach. In: Proceedings of the International 
  Conference on Information, Communications and Signal Processing (ICICS'03), 
  pp. 21--25.

\bibitem[{Hampapur et~al.(1997)Hampapur, Gupta, Horowitz, Shu, Fuller, Bach,
  Gorkani, and Jain}]{SPIESR_1997_Hampapur}
Hampapur, A., Gupta, A., Horowitz, B., Shu, C.-F., Fuller, C., Bach, J.~R.,
  Gorkani, M., Jain, R., 1997. Virage video engine. In: Proceedings of the 
  {SPIE} International Conference on Storage and Retrieval for Image and 
  Video Databases, pp. 188--198.

\bibitem[{Jain(1991)}]{BOOK_1991_Jain}
Jain, R., 1991. The Art of Computer Systems Performance Analysis: Techniques
  for Experimental Design, Measurement, Simulation, and Modeling. John Wiley
  and Sons, Inc.

\bibitem[{Moghaddam et~al.(2002)Moghaddam, Tian, Lesh, Shen, and
  Huang}]{interface-cluster-human-centric02}
Moghaddam, B., Tian, Q., Lesh, N., Shen, C., Huang, T., 2002. {PDH}: a
  human-centric interface for image libraries. In: Proceedings of the 
  {IEEE} International Conference on Multimedia and Expo. pp. 901--904.

\bibitem[{Moghaddam et~al.(2004)Moghaddam, Tian, Lesh, Shen, and
  Huang}]{interface-cluster-human-centric04}
Moghaddam, B., Tian, Q., Lesh, N., Shen, C., Huang, T.~S., 2004. Visualization
  and user-modeling for browsing personal photo libraries. International Journal of Computer Vision 56~(1-2), 109--130.

\bibitem[{Nguyen and Worring(2008)}]{interface-cluster-nguyen}
Nguyen, G.~P., Worring, M., 2008. Interactive access to large image collections
  using similarity-based visualization. Journal of Visual Languages and 
  Computing 19~(2), 203--224.

\bibitem[{Preece et~al.(2002)Preece, Rogers, and Sharp}]{interaction-design}
Preece, J., Rogers, Y., Sharp, H., 2002. Interaction Design. John Wiley \&
  Sons, Inc.

\bibitem[{Snoek et~al.(2007)Snoek, Worring, Koelma, and
  Smeulders}]{TMM_2007_Snoek}
Snoek, C. G.~M., Worring, M., Koelma, D., Smeulders, A. W.~M., 2007. A
  learned lexicon-driven paradigm for interactive video retrieval. {IEEE}
  Transactions on Multimedia 9~(2), 280--292.

\bibitem[{Swain and Ballard(1991)}]{IJCV_1991_Swain}
Swain, M.~J., Ballard, B.~H., Nov. 1991. Color indexing. International Journal
  of Computer Vision 7~(1), 11--32.

\bibitem[{Torres et~al.(2003)Torres, Silva, Medeiros, and
  Rocha}]{CIKM_2003_Torres}
Torres, R.~S., Silva, C.~G., Medeiros, C.~B., Rocha, H.~V., 2003.
  Visual structures for image browsing. In: Proceedings of the {ACM}
  International Conference on Information and Knowledge Management (CIKM'03), 
  pp. 49--55.

\bibitem[{Zavesky and Chang(2008)}]{MIR_2008_Zavesky}
Zavesky, E., Chang, S.-F., 2008. {CuZero}: embracing the frontier
  of interactive visual search for informed users. In: Lew, M.~S., Bimbo, A.,
  Bakker, E.~M. (Eds.), Proceedings of the {ACM} International Workshop on
  Multimedia Information Retrieval (ACM MIR'08), pp. 237--244.

\bibitem[{Zavesky et~al.(2008)Zavesky, Chang, and Yang}]{CIVR_2008_Zavesky}
Zavesky, E., Chang, S.-F., Yang, C.-C., 2008. Visual islands:
  intuitive browsing of visual search results. In: Proceedings of the {ACM}
  International Conference on Image and Video Retrieval (CIVR'08), pp. 617--626.

\end{thebibliography}

\end{document}

%%
%% End of file `elsarticle-template-harv.tex'.
